N8python

#include <stdio.h>
#include <math.h>
#include <time.h>

#define PI 3.14159265358979323846

double s_inv(double x) {
    return asin(2.0 * (x - 0.5)) / PI + 0.5;
}

N8python

import os
import time
from pynput import keyboard
from datetime import datetime
import subprocess
import threading
import tkinter as tk
import queue

# ML imports

N8python

import torch
from torchvision import datasets, transforms
from tqdm import tqdm
import numpy as np

# Import the generated predict function
from predict_function import predict

# Load MNIST test dataset
transform = transforms.Compose([transforms.ToTensor()])

N8python

def predict(i):
    a2=max(0,+i[221]+i[526]+i[565]+i[578]+i[592]+i[612]+i[619]+i[636]+i[637]+i[647])
    a5=max(0,-i[75]+i[98]-i[133]-i[161]-i[162]-i[163]-i[190]-i[191]-i[218]-i[219]-i[220]-i[247]-i[248]-i[276]-i[277]+i[283]+i[294]-i[304]-i[315]-i[332]-i[340]-i[342]-i[445]-i[485]-i[500]-i[528]-i[529]-i[556]-i[557]-i[584]-i[612]+i[676]-i[743]-i[744]-i[745])
    a7=max(0,-i[323]-i[324]-i[325]+i[329]-i[350]-i[352]+i[358]+i[388]+i[412]+i[453]+i[454]+i[456]+i[512])
    a28=max(0,-i[322]-i[348]-i[349]+i[359]+i[370]+i[371]-i[375]-i[376]+i[387]+i[397]+i[398]-i[403]-i[404]+i[427]+i[428]-i[431]-i[435]+i[442]+i[455]+i[456]-i[460]-i[464]+i[484]-i[491]+i[524]+i[748])
    a39=max(0,-i[155]-i[159]-i[377]-i[405]-i[431]-i[432]-i[538]-i[636]+i[677])
    a56=max(0,+i[277]-i[484]-i[485]-i[514]-i[515]-i[516]+i[682])
    a62=max(0,+i[104]+i[122]+i[129]+i[131]+i[132]+i[164]+i[165]+i[191]+i[192]+i[221]+i[250]-i[350]-i[368]+i[416]+i[445]+i[473]+i[528]+i[570]+i[572]-i[649]-i[677]-i[678]-i[679]-i[680]-i[707]-i[708]-i[709]-i[710]-i[711]

N8python

https://0fe3-128-220-159-218.ngrok-free.app/v1/chat/completions

N8python

import OpenAI from "openai";
import fs from "fs";
const culture = [
    "meta-llama/llama-3.1-405b-instruct",
    "openai/gpt-4o",
    "anthropic/claude-3.5-sonnet",
    "qwen/qwen-2-72b-instruct",
    "microsoft/wizardlm-2-8x22b"
];

N8python

import googleIt from 'google-it';
import axios from 'axios';
import cheerio from 'cheerio';
import OpenAI from 'openai';
import readlineSync from 'readline-sync';

const openai = new OpenAI({
    baseURL: "http://localhost:1234/v1",
    apiKey: 'My API Key'
});

N8python

vec3 transmission = vec3(0.0);
float transmissionR, transmissionB, transmissionG;
float randomCoords = rand();
float thickness_smear = thickness * max(pow(roughness, 0.33), anisotropy);
vec3 distortionNormal = vec3(0.0);
vec3 temporalOffset = vec3(time, -time, -time) * temporalDistortion;
if (distortion > 0.0) {
  distortionNormal = distortion * vec3(snoiseFractal(vec3((pos * distortionScale + temporalOffset))), snoiseFractal(vec3(pos.zxy * distortionScale - temporalOffset)), snoiseFractal(vec3(pos.yxz * distortionScale + temporalOffset)));
}
for (float i = 0.0; i < ${samples}.0; i ++) {

N8python

const data = [
    [
        [0, 0], 1
    ],
    [
        [0, 1], 0
    ],
    [
        [1, 0], 0
    ],

N8python

const data = [
    [
        [0, 0], 1
    ],
    [
        [0, 1], 0
    ],
    [
        [1, 0], 0
    ],